
"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE			  =  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE			  =  {
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


SCREAMING_SNAKE_CASE			  =  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class 	__a	(   snake_case_						):
       UpperCamelCase_		:       Tuple				=      VOCAB_FILES_NAMES
       UpperCamelCase_		:       List[Any]				=      PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_		:       Optional[int]				=      PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_		:       str				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_		:       Union[str, Any]				=      SqueezeBertTokenizer






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=None							,   UpperCAmelCase_			:				List[Any]=None							,   UpperCAmelCase_			:				List[Any]=True							,   UpperCAmelCase_			:				Tuple="[UNK]"							,   UpperCAmelCase_			:				Union[str, Any]="[SEP]"							,   UpperCAmelCase_			:				List[str]="[PAD]"							,   UpperCAmelCase_			:				Any="[CLS]"							,   UpperCAmelCase_			:				Any="[MASK]"							,   UpperCAmelCase_			:				int=True							,   UpperCAmelCase_			:				Dict=None							,   **UpperCAmelCase_			:				Dict							,   )->	Dict:







              """simple docstring"""
              super().__init__(
                  lowerCAmelCase__							,   tokenizer_file=lowerCAmelCase__							,   do_lower_case=lowerCAmelCase__							,   unk_token=lowerCAmelCase__							,   sep_token=lowerCAmelCase__							,   pad_token=lowerCAmelCase__							,   cls_token=lowerCAmelCase__							,   mask_token=lowerCAmelCase__							,   tokenize_chinese_chars=lowerCAmelCase__							,   strip_accents=lowerCAmelCase__							,   **lowerCAmelCase__							,   )

              UpperCamelCase       			=		json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
              if (
                  normalizer_state.get("lowercase"							,   lowerCAmelCase__      ) != do_lower_case
                  or normalizer_state.get("strip_accents"							,   lowerCAmelCase__      ) != strip_accents
                  or normalizer_state.get("handle_chinese_chars"							,   lowerCAmelCase__      ) != tokenize_chinese_chars
              ):
                     UpperCamelCase       			=		getattr(lowerCAmelCase__							,   normalizer_state.pop("type"      )      )
                     UpperCamelCase       			=		do_lower_case
                     UpperCamelCase       			=		strip_accents
                     UpperCamelCase       			=		tokenize_chinese_chars
                     UpperCamelCase       			=		normalizer_class(**lowerCAmelCase__      )

              UpperCamelCase       			=		do_lower_case






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Tuple=None      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

              if token_ids_a:
                     output += token_ids_a + [self.sep_token_id]

              return output






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				List[int]							,   UpperCAmelCase_			:				Optional[List[int]] = None      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		[self.sep_token_id]
              UpperCamelCase       			=		[self.cls_token_id]
              if token_ids_a is None:
                     return len(cls + token_ids_a + sep      ) * [0]
              return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Optional[str] = None      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		self._tokenizer.model.save(lowerCAmelCase__							,   name=lowerCAmelCase__      )
              return tuple(lowerCAmelCase__      )










'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




